Airborne time‐domain electromagnetics, electrical resistivity and seismic reflection for regional three‐dimensional mapping and characterization of the Spiritwood Valley Aquifer, Manitoba, Canada
Bibliographic record
Abstract
The Geological Survey of Canada commissioned a helicopter‐borne time‐domain electromagnetic (HTEM) survey over a 1062 km 2 area of the Spiritwood Valley in southern Manitoba in order to test the effectiveness of airborne time‐domain electromagnetics for mapping and characterizing buried valley aquifers in the Canadian Prairies. The data exhibit rich information content and clearly indicate the broader Spiritwood Valley in addition to a continuous incised valley along the broader valley bottom. We detect complex valley morphology with nested scales of valleys including at least three distinct valley features and multiple possible tributaries. Conductivity‐depth images (CDI) derived from the HTEM decays indicate that the fill materials within the incised valleys are more resistive than the broader valley fill, consistent with an interpretation of sand and gravel. Comparison of ground‐based electrical resistivity and seismic reflection data allow for the evaluation of CDI models. Lateral spatial information is in excellent agreement between data sets. However, CDI results tend to underestimate the dynamic range of electrical conductivity while overestimating depths to valley bottoms; these issues may be associated with system limitations, algorithm limitations or differences between data types. The integrated data sets illustrate that HTEM surveys have the potential to map complicated buried valley aquifers at a level of detail required for groundwater prospecting, modelling and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".